Energy Optimization of Robotic Cells

TL;DR

This paper presents a hybrid heuristic approach for energy optimization in industrial robotic cells, achieving significant energy savings and handling larger, more complex systems than previous methods.

Contribution

It introduces a hybrid heuristic method for optimizing energy use in robotic cells, capable of managing larger systems and more optimization factors than prior work.

Findings

Achieved up to 20% energy reduction in real robotic cells.

Solved instances with up to 12 robots, surpassing previous studies.

Heuristic solved 93% of instances with near-optimal solutions.

Abstract

This study focuses on the energy optimization of industrial robotic cells, which is essential for sustainable production in the long term. A holistic approach that considers a robotic cell as a whole toward minimizing energy consumption is proposed. The mathematical model, which takes into account various robot speeds, positions, power-saving modes, and alternative orders of operations, can be transformed into a mixed-integer linear programming formulation that is, however, suitable only for small instances. To optimize complex robotic cells, a hybrid heuristic accelerated by using multicore processors and the Gurobi simplex method for piecewise linear convex functions is implemented. The experimental results showed that the heuristic solved 93 % of instances with a solution quality close to a proven lower bound. Moreover, compared with the existing works, which typically address problems with three to four robots, this study solved real-size problem instances with up to 12 robots and considered more optimization aspects. The proposed algorithms were also applied on an existing robotic cell in \v{S}koda Auto. The outcomes, based on simulations and measurements, indicate that, compared with the previous state (at maximal robot speeds and without deeper power-saving modes), the energy consumption can be reduced by about 20 % merely by optimizing the robot speeds and applying power-saving modes. All the software and generated datasets used in this research are publicly available.